Theorem 2 relates the performance of a source-trained model on a target domain through an upperbound which depends on the distance between the source and the target domain distributions in terms WD distance. We use Theorem 2 to deduce Theorem 1 in the paper. Redko et al. [1]} provide their analysis for the case of binary classifier but their analysis can be extended to multiclas scenario.
